Personalized Driver Map for Accurate ETA in Emerging Markets
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Solution Overview
Problem
Existing routing and estimated time of arrival (ETA) algorithms are not accurate in emerging markets like Southeast Asia, as they do not account for drivers' tendencies to take shortcuts that may not adhere to traffic regulations.
Innovation Solution
A computer-implemented method and system that provides a personalized map for drivers by retrieving a recommended route and identifying historical ride information, which includes driving behavior of previous drivers, to adaptively estimate the time of arrival.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional routing algorithms are used to calculate the most accurate route following legal traffic restrictions, then the route planning is reliable and compliant with traffic rules, but the ETA estimation becomes inaccurate in emerging markets where drivers take shortcuts
Solution Approach 1:
The system applies different routing strategies to different locations and drivers. It creates driver-specific routing models that adapt to individual driving behaviors and local shortcut tendencies, allowing the system to maintain legal compliance in formal routes while accurately predicting actual driver behavior in emerging markets contexts
Solution Approach 2:
The routing algorithm dynamically adjusts based on driver profile and historical data. Instead of using a static legal-compliant route for all drivers, the system adapts the route prediction based on each driver's learned behavior patterns, making the ETA estimation dynamic and personalized to match actual driver tendencies
2Device complexity
If the system implicitly adjusts for shortcuts without visibility into specific shortcuts, then the ETA model remains simple to implement, but the accuracy of ETA prediction deteriorates
Solution Approach 1:
The system performs preliminary analysis of historical ride data to identify and learn driver-specific shortcut patterns before calculating ETA. By pre-processing and storing driver behavior models, the system can quickly apply personalized routing adjustments without complex real-time calculations, balancing model complexity with prediction accuracy
Solution Approach 2:
The system creates a simplified representation (copy) of driver behavior patterns from historical data. Instead of analyzing every possible shortcut scenario in real-time, it uses pre-extracted behavioral models that replicate driver tendencies, reducing computational complexity while maintaining accurate ETA predictions
3Measurement precision
If a personalized map is provided for each driver based on historical ride information, then the ETA estimation becomes highly accurate and adaptive, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the routing problem into driver-specific components. Instead of creating one complex personalized map for all drivers, it divides the problem into individual driver profiles, each with their own learned behavior patterns. This segmentation allows the system to handle complexity at the individual level rather than system-wide
Solution Approach 2:
The system uses drivers' own historical ride data to automatically generate their personalized routing models. Each driver's past behavior serves as training data for their own profile, allowing the system to adapt to individual tendencies without requiring external intervention or complex manual configuration
Data Source
AI summary
A computer-implemented method of adaptively providing an estimated time of arrival and predicting a route likely to be taken by a ride for a ride by providing a personalized map for a driver for the ride. The method comprises: retrieving a recommended route for the ride: identifying historical ride information relating to the driver, the historical ride information identifying a driving behaviour of the driver based on rides that have been taken; and providing a personalized map for the driver based on the retrieved recommended route and the identified historical ride information so as adaptively provide an estimate time of arrival for a ride.